Motion segmentation based on perceptual organization of spatio-temporal volumes

Korimilli, Kishore; Sarkar, Sudeep

doi:10.1109/icpr.2000.903676

Cited by 3 publications

(3 citation statements)

References 14 publications

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“…[8][9][10][11][12][13] show some examples with real sequences. In this case, we have used the values κ 1 = 0, κ 2 = 1, and κ p = 0.5 with the same partial derivatives and weight parameters used in the synthetic case.…”

Section: B Real Sequencesmentioning

confidence: 99%

“…Unlike frame-by-frame analysis (or analysis over a small number of frames), some approaches represent the sequence as a spatio-temporal volume. From this point of view, a moving object may be observed as a three-dimensional (3-D) object, where the x and y axes correspond to the spatial dimensions, and the third axis corresponds to the temporal dimension [12]. Therefore, the goal of this kind of method is to find those spatiotemporal volumes corresponding to significant motions.…”

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confidence: 99%

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A New Approach to Motion Pattern Recognition and Its Application to Optical Flow Estimation

Chamorro-Martı́nez

Fernández-Valdivia

2007

IEEE Trans. Syst., Man, Cybern. C

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Abstract-In this paper, a new methodology for extracting motion patterns is applied to optical flow estimation in the presence of multiple motions. The proposed approximation deals with the problem in two stages. In the first one, the most important motions are segmented; in the second one, the optical flow is estimated on the basis of the motions detected in the previous stage. To extract relevant motions, a new approach based on a spatio-temporal filtering is presented. The approach groups together parts of a moving object that have been separated into various filter responses because of the object's spatial structure, thereby avoiding the spatial dependency problem associated with a representation based on spatio-temporal filters. The proposed model, therefore, generates one "motion pattern" for each motion detected in the sequence. To obtain an optical flow estimation, which is able to represent multiple velocities, the gradient constraint is applied to the output of each filter so that multiple estimations of the velocity at the same location may be obtained. For each "motion pattern" detected in the previous stage, the velocities at a given point corresponding to the same motion are then combined using a probabilistic approach. In the application to optical flow estimation, the use of "motion patterns" allows multiple velocities to be represented, while the combination of estimations from different filters helps reduce the aperture problem. This technique is illustrated on real and simulated data sets, including sequences with occlusion and transparencies.

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Section: B Real Sequencesmentioning

confidence: 99%

mentioning

confidence: 99%

A New Approach to Motion Pattern Recognition and Its Application to Optical Flow Estimation

Chamorro-Martı́nez

Fernández-Valdivia

2007

IEEE Trans. Syst., Man, Cybern. C

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“…From this point of view, a moving object may be observed as a three-dimensional object, where the axis x and y correspond to the spatial dimensions, and the third axis corresponds to the temporal dimension [3]. In this kind of methods, some important proposals are based on a band-pass filtering operation with a set of spatio-temporal filters [4,5,6,7].…”

Section: Introductionmentioning

confidence: 99%

A Spatio-temporal Filtering Approach to Motion Segmentation

Chamorro-Martı́nez

Fdez-Valdivia

Martinez-Baena

2003

Pattern Recognition and Image Analysis

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Abstract. In this paper, a new frequency-based approach to motion segmentation is presented. The proposed technique represents the sequence as a spatio-temporal volume, where a moving object corresponds to a three-dimensional object. In order to detect the "3D volumes" corresponding to significant motions, a new scheme based on a band-pass filtering with a set of logGabor spatio-temporal filters is used. It is well known that one of the main problems of these approaches is that a filter response varies with the spatial orientation of the underlying signal. To solve this spatial dependency, the proposed model allows to recombine information of motions that has been separated in several filter responses due to its spatial structure. For this purpose, motions are detected as invariance in statistical structure across a range of spatio-temporal frequency bands. This technique is illustrated on real and simulated data sets, including sequences with occlusion and transparencies.

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Recognizing Emotions Based on Human Actions in Videos

Wang

2016

MultiMedia Modeling

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Motion segmentation based on perceptual organization of spatio-temporal volumes

Cited by 3 publications

References 14 publications

A New Approach to Motion Pattern Recognition and Its Application to Optical Flow Estimation

A New Approach to Motion Pattern Recognition and Its Application to Optical Flow Estimation

A Spatio-temporal Filtering Approach to Motion Segmentation

Recognizing Emotions Based on Human Actions in Videos

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